CREsted (Cis-Regulatory Element Sequence Training, Explanation and Design): a deep learning package for training enhancer models on single-cell ATAC sequencing (scATAC-seq) data
Output Details
Description
CREsted provides comprehensive analyses and tutorials to study enhancer codes and design synthetic enhancer sequences at cell type-specific, nucleotide-level resolution. CREsted is integrated in the scverse framework and is compatible with outcomes from established scATAC-seq processing tools. It employs novel scATAC-seq preprocessing techniques, such as peak height normalization across cell types, offers flexibility and variety in deep learning modeling architectures and tasks, and contains thorough analysis of cell type-specific enhancer codes captured during modeling that can also be used for the design of synthetic sequences.
Documentation: https://crested.readthedocs.io/en/latest/index.html#
Citation: https://zenodo.org/records/13320756
Example models: https://github.com/aertslab/DeepBrain
Identifier (DOI)
10.5281/zenodo.13320756
